p_slice_max: Slice operations

In dtrackr: Track your Data Pipelines

Slice operations

Description

Slice operations behave as in dplyr, except the history graph can be updated with tracked dataframe with the before and after sizes of the dataframe. See dplyr::slice(), dplyr::slice_head(), dplyr::slice_tail(), dplyr::slice_min(), dplyr::slice_max(), dplyr::slice_sample(), for more details on the underlying functions.

Usage

p_slice_max(
  .data,
  ...,
  .messages = c("{.count.in} before", "{.count.out} after"),
  .headline = "slice data"
)

Arguments

.data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
...	For slice(): <data-masking> Integer row values. Provide either positive values to keep, or negative values to drop. The values provided must be either all positive or all negative. Indices beyond the number of rows in the input are silently ignored. For ⁠slice_*()⁠, these arguments are passed on to methods. Named arguments passed on to dplyr::slice_max .by,by <tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by. .preserve Relevant when the .data input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is. n,prop Provide either n, the number of rows, or prop, the proportion of rows to select. If neither are supplied, n = 1 will be used. If n is greater than the number of rows in the group (or prop > 1), the result will be silently truncated to the group size. prop will be rounded towards zero to generate an integer number of rows. A negative value of n or prop will be subtracted from the group size. For example, n = -2 with a group of 5 rows will select 5 - 2 = 3 rows; prop = -0.25 with 8 rows will select 8 * (1 - 0.25) = 6 rows. order_by <data-masking> Variable or function of variables to order by. To order by multiple variables, wrap them in a data frame or tibble. with_ties Should ties be kept together? The default, TRUE, may return more rows than you request. Use FALSE to ignore ties, and return the first n rows. na_rm Should missing values in order_by be removed from the result? If FALSE, NA values are sorted to the end (like in arrange()), so they will only be included if there are insufficient non-missing values to reach n/prop. weight_by <data-masking> Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1. replace Should sampling be performed with (TRUE) or without (FALSE, the default) replacement.
.messages	a set of glue specs. The glue code can use any global variable, {.count.in}, {.count.out} for the input and output dataframes sizes respectively and {.excluded} for the difference
.headline	a glue spec. The glue code can use any global variable, {.count.in}, {.count.out} for the input and output dataframes sizes respectively.

Value

the sliced dataframe with the history graph updated.

See Also

dplyr::slice_max()

Examples

library(dplyr)
library(dtrackr)


# Subset the data by the maximum of a given value
iris %>% track() %>% group_by(Species) %>%
  slice_max(prop=0.5, order_by = Sepal.Width,
            .messages="{.count.out} / {.count.in} = {prop} (with ties)",
            .headline="Widest 50% Sepals") %>%
  history()


# The narrowest 25% of the iris data set by group can be calculated in the
# slice_min() function. Recording this is a matter of tracking and
# using glue specs.
iris %>%
  track() %>%
  group_by(Species) %>%
  slice_min(prop=0.25, order_by = Sepal.Width,
            .messages="{.count.out} / {.count.in} (with ties)",
            .headline="narrowest {sprintf('%1.0f',prop*100)}% {Species}") %>%
  history()

dtrackr documentation built on Oct. 21, 2024, 5:06 p.m.